Actuator, observations, metrics and traces
Prerequisites: 01-ecosystem
Which signal answers which question?
Goal & mental model verified
Actuator exposes operational endpoints under explicit exposure/security settings. Micrometer Observation can support metrics and tracing. Low-cardinality dimensions suit metric aggregation; high-cardinality identifiers can explode metric series.
[S49] [S50]Worked example · design exercise synthesis
Observe quote latency by route template and outcome. Correlate a slow request through a trace; inspect database wait and JVM events instead of placing every policy ID in a metric label.
[S49] [S50] [S15]Engineering decision synthesis
Design signals around an operational question: errors, latency, saturation and dependency behavior. Expose only required management capabilities and restrict sensitive endpoints.
[S49] [S50] [S15]Pitfall & diagnosis synthesis
Health is not one universal status. Putting shared external outages into liveness can trigger needless restarts; readiness dependencies should reflect whether this instance can serve useful traffic.
[S49] [S50] [S15]Improve & validate synthesis
Simulate slow insurer calls and database outages. Confirm traces propagate and alerts identify the constrained boundary; measure signal cardinality and retention costs.
[S49] [S50] [S15]Check yourself: Should policyId be a metric label?
Usually no. Its high cardinality can create excessive time series; use logs/traces appropriately.